System and method of providing conversational visual prosody for talking heads

ABSTRACT

A system and method of controlling the movement of a virtual agent while the agent is listening to a human user during a conversation is disclosed. The method comprises receiving speech data from the user, performing a prosodic analysis of the speech data and controlling the virtual agent movement according to the prosodic analysis.

RELATED APPLICATIONS

The present application is related to Attorney Docket Number 2002-0027,filed on the same day as the present application, and the contents ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to controlling animations and morespecifically to a system and method of providing reactive behavior tovirtual agents when a human/computer interaction is taking place.

2. Discussion of Related Art

Much work has recently been focused on generating visual Text-to-Speechinteractions between a human user and a computer device. The naturalinteraction between a computer and human is increasing as conversationalagents or virtual agents improve, but the widespread acceptance and useof virtual agents is hindered by un-natural interactions with thevirtual agent. Studies show that a customer's impression of a company'squality is heavily influenced by the customer's experience with thecompany. Brand management and customer relations management (CRM) drivemuch of a company's focus on its interaction with the customer. When avirtual agent is not pleasing to interact with, a customer will have anegative impression of the company represented by the virtual agent.

Movements of the head of a virtual agent must be natural or viewers willdislike the virtual agent. If the head movement is random, thatimpression is more synthetic. In some cases, the head appears to floatover a background. This approach is judged by many viewers to be“eerie.”

One can try to interpret the meaning of the text with a natural languageunderstanding tool and then derive some behavior from that. Yet, such anapproach is usually not feasible, since natural language understandingis very unreliable. A wrong interpretation can do considerable harm tothe animation. For example, if the face is smiling while articulating asad or tragic message, the speaker comes across as cynical or meanspirited. Most viewers dislike such animations and may become upset.

An alternative approach is to use ‘canned’ animation patterns. Thismeans that a few head motion patterns are stored and repeatedly applied.This can work for a short while, yet the repetitive nature of suchanimations soon annoys viewers.

Yet another approach is to provide recorded head movements for thevirtual agent. While this improves the natural look of the virtualagent, unless those head movements are synchronized to the text beingspoken, to the viewer the movements become unnatural and random.

Movement of the head of a virtual agent is occasionally mentioned in theliterature but few details are given. See, e.g., Cassell, J, Sullivan,J. Prevost, S., Churchill, E., (eds.), “Embodied Conversational Agents”,MIT Press, Cambridge, 2000; Hadar, U., Steiner, T. J., Grant, E. C.,Rose, F. C., “The timing of shifts in head postures duringconversation”, Human Movement Science, 3, pp. 237-245, 1984; and Parke,F. I., Waters, K., “Computer Facial Animation”, A. K. Peters, Wellesley,Mass., 1997.

Some have studied emotional expressions of faces and also describenon-emotional facial movements that mark syntactic elements ofsentences, in particular endings. But the emphasis is on head movementsthat are semantically driven, such as nods indicating agreement. See,e.g., Ekman, P., Friesen, W. V., “Manual for the Facial Action CodingSystem”, Consulting Psychologists Press, Palo Alto, 1978.

Conventionally, animations in virtual agents are controlled throughinterpretation of the text generated from a spoken dialog system that isused by a Text-to-Speech (TTS) module to generate the synthetic voice tocarry on a conversation with a user. The system interprets the text andmanually adds movements and expressions to the virtual agent.

Yet another attempt at providing virtual agent movement to illustratedby the FaceXpress development product available for virtual agentsoffered through LifeFX®. The FaceXpress is a tool that enables adeveloper to control the expression of the virtual agent. FIG. 1illustrates the use of the tool 10. In this web-based version of thevirtual agent development tool the developer of the virtual agentorganizes preprogrammed gestures, emotions and moods. Column 12illustrates the selected dialog 14, gestures 16 and other selectablefeatures such as punctuators 32, actions 34, attitudes 36 and moods 38.Column 18 illustrates the selectable features. Shown is column 18 whenthe gestures option is selected to disclose the available pre-programmedgestures smile 20, frown 40 and kiss 42. The developer drags the desiredgesture from column 18 to column 22. Column 22 shows the waveform of thetext 24, a timing ruler 44, the text spoken by the virtual agent 26 androws for the various features of the agent, such as the smile 28. Amoveable amplitude button 46 enables the developer to adjust theparameters of the smile feature. While this process enables thedeveloper to control the features of a virtual agent, it is atime-consuming and costly process. Further, the process clearly will notenable a real-time conversation with a virtual agent where every facialmovement must be generated live. With the increased capability ofsynthetic speech dialog systems being developed using advanced dialogmanagement techniques that remove the necessity for preprogrammedvirtual agent sentences, the opportunity to pre-program virtual agentmovement will increasingly disappear.

The process of manually adding movements to the virtual agent is a slowand cumbersome process. Further, quicker systems do not provide arealistic visual movement that is acceptable to the user. Thetraditional methods of controlling virtual agent movement preclude theopportunity of engaging in a realistic interaction between a user and avirtual agent.

SUMMARY OF THE INVENTION

What is needed in the art is a new method of controlling head movementin a virtual agent such that the agent's movement is more natural andreal. There are two parts to this process: (1) controlling the headmovements of the virtual agent when the agent is talking; and (2)controlling the head movements when the virtual agent is listening. Therelated patent application referenced above relates to item (1). Thisdisclosure relates to item (2) and how to control head movements whenthe virtual agent is listening to a human speaker.

The present invention utilizes prosody to control the movement of thehead of a virtual agent in a conversation with a human. Prosody relatesto speech elements such as pauses, tone, pitch, timing effects andloudness. Using these elements to control the movement of the virtualagent head enables a more natural appearing interaction with the user.

One embodiment of the invention relates to a method of controlling thevirtual agent that is listening to a user. The method comprisesreceiving speech data from the user, performing a prosodic analysis ofthe speech data, and controlling the virtual agent movement according tothe prosodic analysis.

Other embodiments of the invention include a system or apparatus forcontrolling the virtual agent movement while listening to a user and acomputer-readable medium storing a set of instructions for operating acomputer device to control the head movements of a virtual agent whenlistening to a user.

The present invention enables animating head movements of virtual agentsthat are more convincing when a human is having an interactiveconversation. When the facial expressions and head movements of thevirtual agent respond essentially simultaneously to the speech of theuser, the agent appears more like a human itself. This is important forproducing convincing agents that can be used in customer serviceapplications, e.g. for automating call centers with web-based userinterfaces.

Being able to control the facial expressions and head movementsautomatically, without having to interpret the text or the situation,opens for the first time the possibility of creating photo-realisticanimations automatically. For applications such as customer service, thevisual impression of the animation has to be of high quality in order toplease the customer. Many companies have tried to use visualtext-to-speech in such applications, but failed because the quality wasnot sufficient.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing advantages of the present invention will be apparent fromthe following detailed description of several embodiments of theinvention with reference to the corresponding accompanying drawings, inwhich:

FIG. 1 illustrates a prior art method of generating gestures in avirtual agent,

FIG. 2 illustrates ToBI symbols used in marking pitch accents and phraseboundaries in prosody;

FIG. 3 illustrates an exemplary system for controlling movement of avirtual agent during a conversation with a user;

FIG. 4A illustrates an exemplary client/server-based virtual agent modelover a network;

FIG. 4B illustrates another aspect of the client/server-based virtualagent model over a network;

FIG. 5 illustrates tables of pitch accents and phrase boundaries for adata set;

FIG. 6 illustrates the phonetic transcript with annotations of asentence;

FIG. 7 illustrates an example of determining precise head movements fora virtual agent;

FIG. 8 shows the feature points on a virtual agent;

FIG. 8 illustrates a coordinate system used when providing movement to avirtual agent;

FIG. 10A shows an example of the head angel ax as a function of time;

FIG. 10B illustrates a high-pass filtered part of a signal in FIG. 11A;

FIG. 10C shows the same sentence spoken as in FIG. 11B, but with theinstruction to talk with a cheerful expression; and

FIG. 11 illustrates a method of changing databases of virtual agentmovements according to culture or language.

DETAILED DESCRIPTION OF THE INVENTION

In human-to-human conversations, many visual cues aid in the exchange ofinformation and whose turn it is to speak. For example, when a firstperson is speaking and is about to finish, his or her facial expressionand head movement provides visual cues that are recognized by thelistener as indicating that it is the listener's turn to talk. Further,while the first person is speaking, the listener will often exhibit headmotions such as nodding or smiling at the appropriate time to indicatethat the words or meanings provided by the first person are heard andunderstood.

These kinds of very natural movements for humans that assist in anefficient and natural conversation are difficult to replicate when ahuman is speaking to a virtual animated entity or virtual agent.

Head movements, facial expressions, and gestures, applied by the speakerfor underlining the meaning of the text, usually accompany speech. Suchmovements aid the understanding of the spoken text, but they also conveya lot of additional information about the speaker, such as the emotionalstate or the speaker's temper.

Mostly psychologists have studied nonverbal components in face-to-facecommunication extensively. Such studies typically link head and facialmovements or gestures qualitatively to parts of the text. Many of themore prominent movements are clearly related to the content of spokentext or to the situation at hand For example, much of the body languagein conversations is used to facilitate turn taking. Other movements areapplied to emphasize a point of view. Some movements serve basicbiological needs, such as blinking to wet the eyes. Moreover, peoplealways tend to move slightly to relax some muscles while otherscontract. Being completely still is unnatural for humans and requiresconsiderable concentration.

Beside movements that are obviously related to the meaning of the text,many facial expressions and head shifts are tied more to the text'ssyntactic and prosodic structure. For example, a stress on a word isoften accompanied by a nod of the head. A rising voice at the end of aphrase may be underlined with a rise of the head, possibly combined withrising eyebrows. These are the type of facial and head movements usedaccording to the present invention. Since they are analogous to prosodyin speech analysis, these kinds of facial and head movements are called“visual prosody.”

Speech prosody involves a complex array of phonetic parameters thatexpress attitude, assumptions, and attention and can be represented as aparallel channel of information to the meaning of the speech. As such,prosody supports a listener's recovery of the basic message contained inspeech as well as the speaker's attitude toward the message and thelistener as well.

Little information exists about prosodic movements, and no quantitativeresults have been published that show how such head and facial movementscorrelate with elements of speech.

The object of the present invention is to synthesize naturally lookingvirtual agents and especially to synthesize a listening virtual agent aswell as a virtual agent transitioning from listening to talking or fromtalking to listening. The virtual agent may be a person or any kind ofentity such as an animal or other object like a robot.

Many of the classical animation techniques have only limitedapplicability for the types of talking heads we describe here. Artistshave been able since a long time to express emotions and personality incartoon characters with just a few strokes of a pen. See, e.g., Culhane,S, “Animation; From Script to Screen”, Martin's Press, New York, 1988.

However, as talking heads look more and more like real, recorded humans,viewers become more critical of small deviations from what is considerednatural. For example, a cartoon character needs only very roughlip-sound synchronization to be perceived as pleasant. A photo-realistichead, on the other hand, has to show perfect synchronization. Otherwisethe depicted person may seem to have a speech disability, which may beembarrassing to a viewer.

Emulating human behavior perfectly requires an understanding of thecontent of the text. Since many of the movements are closely coupled toprosodic elements of the text, the present invention relates to derivingnaturally looking head movements using just the prosodic information.

Prosody describes the way speech is intonated with such elements aspauses, pitch, timing effects, and loudness. The details of theintonation are influenced by the personality of the speaker, by theemotional state, as well as by the content of the text. Yet, underneathpersonal variations lie well-defined rules that govern the intonation ofa language. See, e.g. Huang, X, Acero, A., Hon, H., Spoken LanguageProcessing Prentice Hall, 2001, pp. 739-791, incorporated herein.Predicting the prosody from text is one of the major tasks fortext-to-speech synthesizers. Therefore, fairly reliable tools exist forthis task.

The text according to the present invention can be recorded or receivedfrom a number of sources. For example, (1) text can be recorded fromshort sentences, such as the one shown in FIG. 7 below, plus greetings;(2) Sentences designed to cover all diphones in English; (3) Shortchildren's stories; and (4) Paragraphs of the Wall Street Journal. Ascan be understood, text can come from any source.

In order to practice the present invention, a database may need to bedeveloped. For example, a database containing about 1,075 sentences wascompiled from recordings of six different speakers. Five speakers talkedfor about 15 minutes each, pronouncing text from the first two sources.The sixth speaker is recorded for over two hours, articulating the wholedata set. In this latter case the speaker is also instructed to speaksome of the text while expressing a number of different emotions.

A prosodic prediction tool identifies prosodic phrase boundaries andpitch accents on the whole database, i.e. labeled the expected prosody.These events are labeled according to the ToBI (Tones and Break Indices)prosody classification scheme. For more information on the ToBI method,see, e.g., M. Beckman, J. Herschberg, The ToBI Annotation Conventions,and K Silverman, M. Beckman, J. Pitrelli, M. Ostemdorf, C. Wightman, P.Price, J. Pierrehumbert, J. Herschberg, “ToBI: A Standard for LabelingEnglish Prosody”, Int. Conf. on Spoken Language Processing 1992, Banff,Canada, pp. 867-870.

ToBI labels do not only denote accents and boundaries, but alsoassociate them with a symbolic description of the pitch movement intheir vicinity. The symbols indicate whether the fundamental frequency(F0) is rising or falling. The two-tone levels, high (H) and low (L),describe the pitch relative to the local pitch range.

FIG. 2 illustrates a first table having a symbol of pitch accent column60 and a corresponding column 62 for the movement of the pitch of thefundamental frequency. For example, H* is a symbol of the pitch accenthaving a corresponding movement of the pitch of high-to-upper end of thepitch range. Similarly, L* is a pitch accent symbol indicating alow-to-lower end of a pitch range.

The second table in FIG. 2 illustrates a phrase boundary column 64 and amovement of the fundamental frequency column 66. This table correlates aphase boundary, such as H-H % to a movement of pitch high and risinghigher toward the end; typical for yes-no question. From correlationssuch as this, the ToBI symbols for marking pitch accents and phraseboundaries can be achieved. These pitch accents and phase boundaries canbe used to control the movement of the virtual agent while listening andspeaking to a user.

A conversation with a talking head will appear natural only if not justthe speaking, or active, portion of the conversation is animatedcarefully with appropriate facial and head movements, but also thepassive, or listening part. Tests with talking heads indicate that oneof the most frequent complaints relate to appropriate listeningbehavior.

The present invention addresses the issue of how to control the movementand expression on the face of an animated agent while listening. Theinvention solves the problem by controlling facial and head movementsthrough prosodic and syntactic elements in the text entered by a user,i.e. the text that the talking head is supposed to listen to and‘understand’. Adding listening visual prosody, i.e. proper facial andhead movements while listening, makes the talking head seem tocomprehend the human partner's input.

FIG. 3 illustrates a system 100 or apparatus for controlling aconversation between a user and a virtual agent, including the movementof the virtual agent while listening. As is known in the art, thevarious modules of the embodiments of the invention may operate oncomputer devices such as a personal computer, handheld wireless device,a client/server network configuration or other computer network. Theparticular configuration of the computer device or network, whetherwireless or not, is immaterial to the present invention. The variousmodules and functions of the present invention may be implemented in anumber of different ways.

Text or speech data is received from a source 102. The source may bewords spoken by a user in a conversation with a virtual agent or fromany other source. From this speech data, a prosodic analysis module 104performs a syntactic analysis to determine and extract prosodic andsyntactic patterns with the speech data.

After the prosodic structure of an utterance has been determined, thisinformation has to be translated into movements of the head and facialparts. One way is to store many prosodic patterns and theircorresponding head movements in a database. In a speaking, listening ortransition mode for the virtual agent, when the system prepares tosynthesize a sentence to be spoken by the virtual agent, the systemsearches for a sample in the database with similar prosodic events andselect the corresponding head and facial movements. This produces verynaturally looking animations. The aspect of the invention does require alarge number of patterns have to be stored and the whole database has tobe searched for every new animation.

The precise form of the head and facial movements is not critical andvaries widely from person to person. What matters is that the movementsare synchronized with the prosodic events. Such movements can begenerated with a rule-based model or a finite state machine. For thisapproach, the inventors analyzed recorded sequences and determined theprobability of particular head movements for each of the main prosodicevents. For example, the system looks at how often a nod is happening ona stress, or a raised eyebrow at the end of a question. Using such amodel, the system calculates for each prosodic event in the sequence thelikelihood that a prominent head movement occurs. This model orrule-based approach can produce naturally looking sequences if enoughsamples are analyzed to determine all the probabilities properly. It hasthe advantage that it is computationally less costly than a sample-basedapproach.

Returning to FIG. 3, the prosodic data is transmitted to a selectionmodule 106 that selects associated or matching prosody or syntacticpatterns from a listening database 108. The listening database storesprosodic and syntactic patterns, as well as behavior patterns, that areappropriate for listening activity according to convention. Once theselection module 106 selects the behavior patterns, the patterns aretransmitted to the virtual agent-rendering device as listening andbehavioral face and head movements 110.

As an example, if a user were to elevate his voice in a conversationwith another human, the listening person may naturally pull back andraise his eyebrows at the outburst. Similarly, the listening databasewill store such behavioral patterns for appropriate responses to thedetected prosody in the speech data directed at the virtual agent.

Once the selection module 106 selects the behavioral patterns, the datais transmitted to the virtual agent in real-time to control thelistening behavior, i.e., facial and head movements, of the virtualagent. For example, suppose a person is talking in a monotone voice forseveral minutes to the virtual agent. The behavior of the virtual agentaccording to the prosody of the monotone speech will be appropriate forsuch language. This may be, for example, minor movements and eyeblinking. However, if the person suddenly yells at the virtual agent,the sudden change in the prosody of the speech would be immediatelyprocessed, and a change in the listening behavior pattern would shift inthe virtual agent, and the virtual agent would exhibit a surprised oreye-brow-raising expression in response to the outburst.

In addition to listening and speaking behavior, there are alsotransition periods, which must be animated appropriately. When the inputtext 102 stops, the behavior has to switch to a ‘planning’ or‘preparation’ stage, where it is clear that the head is getting readyfor a response. Control moves from the listening selection module 106 tothe transition selection module 112.

The selection module 112 controls the interaction between the transitiondatabase 114 and the movement of the virtual agent 116. The selectionmodule 112 matches prosody and syntactic patterns drawn from atransition database 114. The transition database stores behaviorpatterns appropriate for transition behaviors. For example, when onespeaker is done, certain movements of the head or behaviors willindicate the other person's turn to talk. If one person continues totalk and the other wants to speak, a certain transition visual behaviorwill indicate a desire to cut in and talk. Once the transition patternis selected, the selection module 112 transmits the data to the virtualagent for controlling the behavioral facial and head movements inreal-time for a more natural experience for the user.

Once a transition is complete, the conversation proceeds to the virtualagent's turn to talk. Here, the selection module 118 will receive aphoneme string having prosodic and syntactic patterns 124 from a textgeneration module 126. Those of skill in the art are aware of means fortext generation in the context of a spoken dialog service.

The speaking movement selection module 118 uses the prosodic andsyntactic patterns from the generated text to select from a speakingdatabase 120 the appropriate prosodic and syntactic behavioral patternsto control the speaking behavior and facial movements of the virtualagent 122. At the end of text output from the virtual agent, the virtualagent should signal to the viewer that now it is his or her turn tospeak.

The speaking database may comprise, for example, an audio-visualdatabase of recorded speech. The database may be organized in a varietyof ways. The database may include segments of audio-visual speech of aperson reading text that includes an audio and video component. Thus,during times of listening, speaking or transition, the system searchesthe speaking database and selects segments of matching visual prosodypatters from the database and the system controls the virtual agentmovements according to the movements of the person recorded in theselected audio-visual recorded speech segments. In this manner, the usercan experience a more realistic and natural movement of the virtualagent.

In another aspect of the invention, the speaking behavior database isnot utilized and a model is used for determining virtual agent movementsaccording to speech data. The model may be automatically trained or behandcrafted. Using the model approach, however, provides a differentmeans of determining virtual agent movement based on speech data thanthe look-up speaking database. Similar models may be employed forlistening movements as well as transition movements. Thus, there are avariety of different ways wherein a system can associate and coordinatevirtual agent movement for speaking, listening and transition segmentsof a human-computer dialog.

A conversation control module 128 controls the interaction between thetext generation module 126 (voice and content of virtual agent) andreceiving the text or speech data from the user 102. These modulespreferably exist and are operational on a computer server or servers.The particular kind of computer device or network on which these modulesrun is immaterial to the present invention. For example, they may be onan intranet, or the Internet or operational over a wireless network.Those of skill in the art will understand that other dialog modules arerelated to the conversation module. These modules include an automaticspeech recognition module, a spoken language understanding module, adialog management module, a presentation module and a text-to-speechmodule.

FIG. 4A illustrates an aspect of the invention in a network context.This aspect relates to a client/server configuration over a packetnetwork, Internet Protocol network, or the Internet 142. Further, thenetwork 142 may refer to a wireless network wherein the client device140 transmits via a wireless protocol such as Bluetooth, GSM, CDMA, TDMAor other wireless protocol to a base station that communicates with theserver 144A. U.S. Pat. No. 6,366,886 B1, incorporated herein byreference, includes details regarding packet networks and ASR overpacket networks.

In FIG. 4A, the prosodic analysis for both the virtual agent listening,transition, and response mode is performed over the network 142 at aserver 144A. This may be the requirement where the client device is asmall hand-held device with limited computing memory capabilities. As anexample of the communication in this regard, the client device includesmeans for receiving speech from a user 139. This, as will be understoodby those of skill in the art, may comprise a microphone and speechprocessing, voice coder and wireless technologies to enable the receivedspeech to the transmitted over the network 142 to the server 144A. Acontrol module 141 handles the processes required to receive the userspeech and transmit data associated with the speech across the network142 to the server 144A.

The server 144A includes a prosodic analysis module 146 that analyzesthe prosodic elements of the received speech. According to this prosodicanalysis, in real-time, a listening behavior module 148 in the server144A transmits data associated with controlling the head movements ofthe virtual agent 160. Thus, while the user 139 speaks to the virtualagent 160, it appears that the agent is “listening.” The listeningbehavior includes any behavior up to and through a transition fromlistening to preparing to speak. Therefore, data transmitted as shown inmodule 148 includes transition movements from listening to speaking.

Next, a response module 150 generates a response to the user's speech orquestion. The response, as is known in the art, may be generatedaccording to processes performed by an automatic speech recognitionmodule, a spoken language understanding module, a dialog manager module,a presentation manager, and/or a text-to-speech module. (FIG. 4B showsthese modules in more detail). As the response is transmitted to theclient device 140A over the packet network 150, the server 144A performsa prosodic analysis on the response 152 such that the client device 140Areceives and presents the appropriate real-time responsive behavior suchas facial movements and expressions 154 of the virtual agent 160associated with the text of the response.

A realistic conversation, including the visual experience of watching avirtual agent 160 on the client device 140A, takes place between theuser and the virtual agent 160 over the network 142.

The transmission over a network 142 such as a packet network is notlimited to cases where prosody is the primary basis for generatingmovements. For example, in some cases, the virtual agent 160 responsesare preprogrammed such that the response and the virtual agent 160motion are known in advance. In this case, then the data associated withthe response as well as the head movements are both transmitted over thenetwork 142 in the response phase of the conversation between the userand the virtual agent 160.

FIG. 4B illustrates another aspect of the network context of the presentinvention. In this case, the client device 140B includes a controlmodule 143 that receives the speech from the user 139. The controlmodule transmits the speech over the network 142 to the server 144B.Concurrently, the control module transmits speech data to a prosodicanalysis module 145. The listening behavior is controlled in this aspectof the invention on the client device 140B. Accordingly, while theperson 139 is speaking, the modules on the client device 140B controlthe prosodic analysis, movement selection, and transition movement. Bylocally processing the listening behavior of the virtual agent 160, amore real-time experience is provided to the user 139. Further, thisisolates the client device and virtual agent listening behavior fromnetwork transmission traffic slow-downs.

The server 144B performs the necessary processing to carry on a dialogwith the user 139, including automatic speech recognition 149, spokenlanguage understanding 151, dialog management 153, text-to-speechprocessing 155, and prosodic analysis and virtual agent movement control157 for the response of the virtual agent 160.

The present aspects of the invention are not limited to the specificprocessing examples provided above. The combination of prosodic analysisand other ASR, SLU, DM and TTS processes necessary to carry out a spokenand visual dialog with the user 139 may be shared in any combinationbetween the client device 140B and the server 144B.

In another aspect of the invention not shown in FIG. 4B, the prosodicanalysis module can control the virtual agent movement both for when thevirtual agent listens and speaks. In this variation of the invention,preferably, the prosodic analysis module 145 on the client devicereceives and analysis the TTS speech data from the server 144B. Themovement of the virtual agent 160 while the virtual agent is speaking ortransitioning from speaking to listening or listening to speaking isthus entirely controlled by software operating on the client device140B. The movement control module 157 on the server may or may not beoperative or exist in this aspect of the invention since all movementbehavior is processed locally.

Recording real people and correlating their behavior with the prosodicinformation in the text enables the automation of the process ofgenerating facial expressions and head movements. Prosodic informationcan be extracted reliably from text without the need of a high-levelanalysis of the semantic content. Measurements confirm a strongcorrelation between prosodic information and behavioral patterns. Thisis true for the correlation between behavioral patterns and the textspoken by a person, but also for the correlation between text spoken byone person and the behavioral patterns of the listener.

Accents within spoken text are prime candidates for placing prominenthead movements. Hence, their reliable identification is of main interesthere. Stress within isolated words has been compiled in lexica for manydifferent languages. Within continuous speech, however, the accents arenot necessarily placed at the location of the lexical stress. Context orthe desire to highlight specific parts of a sentence may shift the placeof an accent. It is therefore necessary to consider the whole sentencein order to predict where accents will appear.

Any interruption of the speech flow is another event predestined forplacing head or facial movements. Many disfluencies in speech areunpredictable events, such as a speaker's hesitations. ‘ah’ or ‘uh’ areoften inserted spontaneously into the flow of speech. However, othershort interruptions are predictably placed at phrase boundaries.Prosodic phrases, which are meaningful units, make it easier for thelistener to follow. That is why prosodic phrase boundaries oftencoincide with major syntactic boundaries and punctuation marks. FIG. 5shows the types of boundaries predicted by the prosody tool, and howoften they appear in the text. With each phrase boundary, a specifictype of pitch movement is associated. This is of special interest heresince it allows, for example, adding a rise of the head to a risingpitch. Such synchronizations can give a virtual agent the appearance ofactually ‘understanding’ the text being spoken by the virtual agent.Identifying these types of boundaries can further provide the real-timeappearance of actively listening to the speaking user.

As shown in FIG. 5, column 190 illustrates the pitch accents, such asH*, and column 192 shows the corresponding number of events for the dataset of 1075 sentences. Column 194 shows the phrase boundary such as L-L% and the corresponding column 196 shows the number of events in thedata set.

FIG. 6 illustrates the phonetic transcription and prosodic annotation ofthe sentence “I'm your virtual secretary.” Column 200 illustrates thetime until the end of the phone. Column 202 shows the correspondingphone. Column 204 shows the prosodic event, where applicable for aphone. Column 206 illustrates the associated word in the sentence to thetime, phone and prosodic event of the other three columns.

In this case, the phone durations in column 200 were extracted from thespoken text with a phone-labeling tool. Alternatively, the prosodyanalysis tools can predict phone durations from the text. Accents areshown here at the height of the last phone of a syllable, but it has tobe understood that the syllable as a whole is considered accented andnot an individual phone.

Of the different accent types, the H* accents strongly dominate (compareFIG. 2). Moreover, the prediction of the other types of accents is notvery reliable. Studies show that even experienced human labelers agreein less than 60% on the accent types. Therefore the present inventiondoes not differentiate between the various types of pitch accents andlump them all together simply as accents.

The prosody predictor according to research associated with the presentinvention has been trained with ToBI hand labels for 1,477 utterances ofone speaker. The accent yes/no decision is correct in 89% of allsyllables and the yes/no decision for phrase boundaries in 95%. Theaccent types are predicted correctly in 59% of all syllables and theboundary types in 74% of all cases. All the speakers recorded aredifferent from the speaker used to train the prosody predictor. Onevoice can be used as well to train for prosody prediction.

Associated with the present invention is gathering data on facerecognition from human readers. Natural head and facial movements whilereading text provide the information for generating the head movementsof a virtual agent. Hence, when using a human speaker, the speakers mustbe able to move their heads freely while they pronounce text. It ispreferable that no sensors on the person's head be used while gatheringthe human data. The natural features of each human face are used sinceno markers or other artifacts for aiding the recognition systems areused.

In an exemplary data gathering method, recordings are done with thespeaker sitting in front of a teleprompter, looking straight into acamera. The frame size is 720×480 pixels and the head's height istypically about ⅔ of the frame height. The total of the recordingscorresponds to 3 hours and 15 minutes of text. Facial features areextracted from these videos and head poses for each of over 700,000frames. Recordings may be done at, for example, 60 frames per second.

In order to determine the precise head movements as well as themovements of facial parts, the positions of several facial featurepoints must be measured with a high accuracy. A face recognition systemaccording to the present invention proceeds in multiple steps, each onerefining the precision of the previous step. See, e.g., “Face Analysisfor the Synthesis of Photo-Realistic Talking Head,” Graf, H. P.,Cosatto, E. and Ezzat, T., Proc. Fourth IEEE Int. Conf. Automatic Faceand Gesture Recognition. Grenoble, France, IEEE Computer Society, LosAlamitos, 2000, pp. 189-194.

Using motion, color and shape information, the head's position and thelocation of the main facial features are determined first with a lowaccuracy. Then, smaller areas are searched with a set of matchedfilters, in order to identify specific feature points with highprecision. FIG. 7 shows an example of this process using a portion of avirtual agent face 210. Representative samples of feature points maycomprise, for example, eye corners 212 and 214 with corresponding points212 a and 214 a on the image 210 and eye edges 216 and 218 withcorresponding reference points 216 a and 218 a on the image 210. Theseimages 212, 214, 216 and 218 are cut from image 210. By averaging threeof these images and band-pass filtering the result, these sample imagesor kernels become less sensitive to the appearance in one particularimage. Such sample images or filter kernels are scanned over an area toidentify the exact location f a particular feature, for example, an eyecorner. A set of such kernels is generated to cover the appearances ofthe feature points in all different situations. For example, ninedifferent instances of each mouth corner are recorded, covering threedifferent widths and three different heights of the mouth.

When the system analyzes a new image, the first steps of the facerecognition, namely shape and color analysis, provide information suchas how wide open the mouth is. One can therefore select kernels of mouthcorners corresponding to a mouth of similar proportions. Image andkernels are Fourier transformed for the convolution, which iscomputationally more efficient for larger kernels. In this way a wholeset of filter kernels is scanned over the image, identifying the featurepoints.

The head pose is calculated from the location of the eye corners and thenostrils in the image. FIG. 8 shows an example of identified featurepoints 232 in the image and a synthetic face model 234 in the same pose.Under these conditions, the accuracy of the feature points 323 must bebetter than one pixel; otherwise the resulting head pose may be off bymore than one degree, and the measurements become too noisy for areliable analysis.

There is a tradeoff between accuracy and selectivity of the filters.Larger filter kernels tend to be more accurate, yet they are moreselective. For example, when the head rotates, the more selectivefilters are useable over a smaller range of orientations. Hence, moredifferent filters have to be prepared. Preferably, the system typicallytunes the filters to provide an average precision of between one and oneand a half pixels. Then the positions are filtered over time to improvethe accuracy to better than one pixel. Some events, for example eyeblinks, can be so rapid that a filtering over time distorts themeasurements too much. Such events are marked and the pose calculationis suspended for a few frames.

Beside the head pose, the present invention focuses on the positions ofthe eyebrows, the shape of the eyes and the direction of gaze. Thesefacial parts move extensively during speech and are a major part of anyvisual expression of a speaker's face. They are measured with similarfilters as described above. They do not need to be measured with thesame precision as the features used for measuring head poses. Whethereyebrows move up one pixel more or less does not change the face'sappearance markedly.

The first part of our face analysis, where the head and facial parts aremeasured with a low accuracy, works well for any face. Sufficientredundancy is built into the system to handle even glasses and beards.The filters for measuring feature positions with high accuracy, on theother hand, are designed specifically for each person, using samples ofthat person's face.

For identifying prosodic movements, the rotation angles of the headaround the x-, y-, and z-axis are determined, together with thetranslations. FIG. 9 shows the orientation of the coordinate system usedfor these measurements. For example, ax, ay, az mark the rotationsaround the x, y, z axes.

All the recorded head and facial movements was added spontaneously bythe speakers while they were reading from the teleprompter. The speakerswere not aware that the head movements would be analyzed. For most ofthe recordings the speakers were asked to show a ‘neutral’ emotionalstate.

For the analysis, each of the six signals representing rotations andtranslations of the head are split into two frequency bands: (1) 0-2 Hz:slow head movements and (2) 2 Hz-15 Hz: faster head movements associatedwith speech.

Movements in the low frequency range extend over several syllables andoften over multiple words. Such movements tend to be caused by a changeof posture by the speaker, rather than being related to the speech.FIGS. 10A-10C are examples of the head angle ax as a frequency of time.FIG. 10A shows a graph 250 of the original signal 252 and a low-passfiltered signal 254. The time on the horizontal axis is given in framenumbers with 30 frames a second.

The faster movements, on the other hand, are closely related to theprosody of the text. Accents are often underlined with nods that extendtypically over two to four phones. This pattern is dearly visible inFIG. 10B. In this graph 260, the data 262 represents the high-passfiltered part of the original signal 254 in FIG. 10A. The markings inFIG. 10B represent phone boundaries with frame numbers at the top of thegraph. Phones and prosodic events are shown below the graph. Here thenods are very clearly synchronized with the pitch accents (positivevalues for angle ax correspond to down movements of the head). Typicalfor visual prosody, and something observed for most speakers, is thatthe same motion—in this case a nod—is repeated several times. Not onlyare such motion patterns repeated within a sentence, but often over anextended period of time; sometimes as much as whole recording session,lasting about half an hour.

A further characteristic feature of visual prosody is the initial headmovement, leading into a speech segment after a pause. FIG. 10B showsthis as a slight down movement of the head (ax slighdy positive) 164,followed by an upward nod at the start of the sentence 266. Whiledeveloping the present invention, the applicant recorded 50 sentences ofthe same type of greetings and short expression in one recordingsession. The speaker whose record is shown in FIGS. 10A-10B executed thesame initial motion pattern in over 70% of these sentences.

In FIGS. 10A-10C, only the rotation around the x-axis, ax, is shown. Inthis recording the rotation ax, i.e. nods, was by far the strongestsignal. Many speakers emphasize nods, but rotations around the y-axisare quite common as well, while significant rotations around the z-axisare rare. A combination of ax and ay, which leads to diagonal headmovements, is also observed often.

The mechanics for rotations around each of the three axes are differentand, consequently, the details of the motion patterns vary somewhat.Yet, the main characteristics of all three of these rotations aresimilar and can be summarized with three basic patterns:

-   -   1. Nod, i.e. an abrupt swing of the head with a similarly abrupt        motion back. Nod with an overshoot at the return, i.e. the        pattern looks like an ‘S’ lying on its side.    -   2. Abrupt swing of the head without the back motion. Sometimes        the rotation moves slowly, barely visible, back to the original        pose, sometimes it is followed by an abrupt motion back after        some delay.

Summarizing these patterns, where each one can be executed around thex-, y-, or z-axis:

-   -   ˆ nod (around one axis)    -   ˜ nod with overshoot    -   / abrupt swing in one direction

Having such motion primitives allows describing head movements with theprimitives' types, amplitudes and durations. This provides a simpleframework for characterizing a wide variety of head movements with justa few numbers. Table 1 shows some statistical data of the appearance ofthese primitives in one part of the text database. This illustrates thepercentage of pitch accents accompanied by a major head movement. Thetext corpus associated with this table was 100 short sentences andgreetings. TABLE 1 P({circumflex over ( )}_(x) | *) 42% P(˜_(x) | *) 18%P (/_(x) | *) 20%

The amplitudes of the movements can vary substantially, as isillustrated by the graph 270 in FIG. 10C. For this recording the speakerwas asked to articulate the same sentence as in FIG. 10B, but with acheerful expression. The initial head motion is a wide down and up swingof the head 274, which runs over the first nod seen in FIG. 10B. Thefirst nod falls down on the second accent 176 and the sentence ends withan up-down swing 178.

The patterns described here are not always visible as clearly as in thegraphs of FIG. 10A-10C. Some speakers show far fewer prosodic headmovements than others. The type of text also influences prosodic headmovements. When reading paragraphs from the Wall Street Journal, thehead movements were typically less pronounced than for the greetingsentences. On the other hand, when speakers have to concentratestrongly, while reading a demanding text, they often exhibit often veryrepetitive prosodic patterns.

Head and facial movements during speech exhibit a wide variety ofpatterns that depend on personality, mood, content of the text beingspoken, and other factors. Despite large variations from person toperson, patterns of head and facial movements are strongly correlatedwith the prosodic structure of the text. Angles and amplitudes of thehead movements vary widely, their timing shows surprising consistency.Similarly, rises of eyebrows are often placed at prosodic events,sometimes with head nods, at other times without. Visual prosody is notnearly as rigidly defined as acoustic prosody, but is clearlyidentifiable in the speech of most people.

Recent progress in face recognition enables an automatic registration ofhead and facial movements and opens the opportunity to analyze themquantitatively without any intrusive measuring devices. Such informationis a key ingredient for further progress in synthesizing naturallylooking talking heads. Lip-sound synchronization has reached a stagewhere most viewers judge it as natural. The next step of improvementlies in realistic behavioral patterns. Several sequences weresynthesized where the head movements consisted of concatenations of themotion primitives described above. With good motion-prosodysynchronization the heads look much more engaging and even give theillusion that they ‘understand’ what they articulate and actively listento the speaker.

Prosody prediction tools exist for several languages and the presentinvention is applicable to any language for which such tools areavailable. Even if there are no prosody tools available for a particularlanguage, it may be possible to generate one. Typically the generationof a prosody prediction tool is much simpler than a languageunderstanding tool. If no good prosody prediction tools exist, manyprosodic elements can still be predicted from the syntactic structure ofthe text. Therefore the concept of visual prosody is applicable to anylanguage.

In another aspect of the invention, it is noted that the basic elementsof prosody such as pauses, pitch, rate or relative duration, andloudness, are culturally driven in that different cultures will attachdifferent meanings to prosodic elements. Accordingly, an aspect of thisinvention is to provide a system and method of adapting prosodicspeaking, transition and listening movements of a virtual agent thatadapt to the appropriate culture of the speaker.

Further gradations may include differences in dialect or accents whereinthe prosodic responses may be different, for example, between a personfrom New York City and Georgia.

In this embodiment of the invention, a database of speaking, transition,and listening movements as described above is compiled for each set ofcultural possibilities. For example, an English set is generated as wellas a Japanese set. As shown in FIG. 11, a language determination modulewill determine the language of the speaker (280). This may be via speechrecognition or via a dialog with the user wherein the user indicateswhat language or culture is desired.

Suppose the speaker selects Japanese via the determination module. Thesystem then knows that to enable a natural looking virtual agent in theconversation using the prosodic nature of the speech it will receive,that the appropriate set of spealdng, listening and transition databasesmust be selected. An example of such a change may be that the virtualagent would bow at the culturally appropriate times in a conversationwherein if the user is from a different culture than those agentmovements would not be displayed during the conversation. A selectionmodule then selects that appropriate set of databases (282) for use inthe conversation with the speaker.

The system proceeds then to modify, if necessary, the prosodic-drivenmovements of the virtual agent according to the selected databases (284)such that the Japanese user will experience a more natural conversation.The system also operates dynamically wherein if the user part-waythrough a conversation switches to English, ends the conversation (288),or indicates a different cultural set, then the process returns to thedetermining language module for making that switch (280), and thencontinuing to select the appropriate set of databases for proceedingwith the conversation with the user.

The number of databases is only limited by the storage space. Databasesfor Spanish, English with a New York Accent, English with a SouthernAccent, Japanese, Chinese, Arabic, French, Senior Citizen, Teenager,Ethnicity, etc. may be stored and ready for the specific culturalmovement that will appeal most to the particular user.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. For example, any electronic communication between peoplewhere an animated entity can deliver the message is contemplated. Emailand instant messaging have been specifically mentioned above, but otherforms of communication are being developed such as broadband 3G and 4Gwireless technologies wherein animated entities as generally describedherein may apply. Further, visual prosody contains a strong personalitycomponent and some trademark movements may be associated with certainpersonalities. The system can be trained to exhibit prosodic behavior ofa particular person or one that is considered more ‘generic’.Accordingly, the appended claims and their legal equivalents should onlydefine the invention, rather than any specific examples given.

1. A method of providing user-specific animated entity movements duringa conversation with the user, the method comprising: determining aparameter associated with the user; based on the parameter, selecting adatabase of data associated with movements of an animated entity; andcontrolling movement of the animated entity when the animated entity islistening to the user during a conversation according to the selecteddatabase, wherein the virtual agent movement appears to respond to thereceived speech data.
 2. The method of providing user-specific animatedentity movements during a conversation with the user of claim 1, whereinthe parameter associated with the user is further associated with aculture of the user.
 3. The method of providing user-specific animatedentity movements during a conversation with the user of claim 1, whereinthe parameter associated with the user is further associated with alanguage spoken by the user.
 4. The method of providing user-specificanimated entity movements during a conversation with the user of claim1, wherein the parameter associated with the user is further associatedwith an age of the user.
 5. The method of providing user-specificanimated entity movements during a conversation with the user of claim1, wherein the parameter associated with the user is further associatedwith a dialect spoken by the user.
 6. The method of providinguser-specific animated entity movements during a conversation with theuser of claim 1, wherein the parameter associated with the user isfurther associated with an ethnicity of the user.
 7. A computing devicefor providing user-specific animated entity movements during aconversation with the user, the computing device comprising: a moduleconfigured to determine a parameter associated with the user; a moduleconfigured to, based on the parameter, select a database of dataassociated with movements of an animated entity; and a module configuredto control movement of the animated entity when the animated entity islistening to the user during a conversation according to the selecteddatabase, wherein the virtual agent movement appears to respond to thereceived speech data.
 8. The computing device of claim 7, wherein theparameter associated with the user is further associated with a cultureof the user.
 9. The computing device of claim 7, wherein the parameterassociated with the user is further associated with a language spoken bythe user.
 10. The computing device of claim 7, wherein the parameterassociated with the user is further associated with an age of the user.11. The computing device of claim 7, wherein the parameter associatedwith the user is further associated with a dialect spoken by the user.12. The computing device of claim 7, wherein the parameter associatedwith the user is further associated with an ethnicity of the user.
 13. Acomputer-readable medium storing instructions for controlling acomputing device to provide user-specific animated entity movementsduring a conversation with the user, the instructions comprising:determining a parameter associated with the user; based on theparameter, selecting a database of data associated with movements of ananimated entity; and controlling movement of the animated entity whenthe animated entity is listening to the user during a conversationaccording to the selected database, wherein the virtual agent movementappears to respond to the received speech data.
 14. Thecomputer-readable medium of claim 13, wherein the parameter associatedwith the user is further associated with a culture of the user.
 15. Thecomputer-readable medium of claim 13, wherein the parameter associatedwith the user is further associated with a language spoken by the user.16. The computer-readable medium of claim 13, wherein the parameterassociated with the user is further associated with an age of the user.17. The computer-readable medium of claim 1, wherein the parameterassociated with the user is further associated with a dialect spoken bythe user.
 18. The computer-readable medium of claim 1, wherein theparameter associated with the user is further associated with anethnicity of the user.